Parkinson's Disease Speech Classification Using 1D Convolutional Neural Networks

Zhen Yue; Haiyang Wang

doi:10.54097/agw6qc09

Authors

Zhen Yue
Haiyang Wang

DOI:

https://doi.org/10.54097/agw6qc09

Keywords:

Parkinson Disease, Speech Features, Convolutional Neural Networks

Abstract

Parkinson's Disease (PD) is a neurodegenerative disorder caused by a lack of dopamine secretion. Both motor and non-motor activities of Parkinson's patients are affected. This study proposes a method for Parkinson's disease audio feature classification based on Convolutional Neural Networks (CNN). By extracting features from the speech signals of Parkinson's patients, and leveraging the powerful feature extraction and classification capabilities of CNNs, an efficient diagnosis of Parkinson's disease can be achieved. To evaluate the performance of the proposed method, experiments were conducted on two datasets, achieving accuracy rates of 100% and 92.86%, respectively.

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